PCA特征提取和弹性BP神经网络的水下气泡识别

针对水下激光雷达探测得到的尾流回波信号由于非稳态造成特征提取困难、不易识别的问题，提出了基于PCA特征提取与弹性BP神经网络结合的水下气泡识别算法。首先对连续采集的回波信号进行切片预处理，然后采用PCA算法对拼接的高维样本进行主要特征提取，确定特征值个数，其次对弹性BP神经网络进行参数的选择，确定能实现最优分类的隐含层节点数、特征个数等，最后根据室内搭建的尾流探测模拟平台，实现对气泡群和干扰目标的识别。实验结果表明:在隐含节点为12，增量因子为1.15，减量因子为0.55时，选取两个特征值能对有气泡、无气泡及干扰物进行有效分类；识别率随着气泡群密度的增大提升13.4%，在低密度下的识别率随激光能量的增加平均提升6.3%，识别率随距离的增加先增大后减小，气泡群在2.2 m时的目标峰特征明显，平均识别率提升3.5%。通过与自适应附加动量BP对比，该方法在减少识别时间的同时准确率达到99.1%，证明该算法可有效运用于激光雷达舰船尾流气泡的识别。

Underwater bubbles recognition based on PCA feature extraction and elastic BP neural network

Aiming at the problem of difficulty in feature extraction and identification of wake echo signals detected by underwater lidar due to instability, an underwater bubbles recognition algorithm based on PCA feature extraction and elastic BP neural network was proposed. First, slice preprocessing was carried out on the echo signals collected continuously. Then the PCA algorithm was used to extract the main features of the spliced high-dimensional samples to determine the number of feature values. Then the parameters of the elastic BP neural network was selected to determine the number of hidden layer node and the number of features that can achieve optimal classification. Finally, an indoor wake detection simulation platform was used to realize the identification of bubbles and interfering targets. The experimental results show that when the hidden node is 12, the increment factor is 1.15, and the decrement factor is 0.55, two eigenvalues can be selected to classify the bubbles, non-bubbles and interfering targets. The recognition rate increases by 13.4% with the increase of bubbles density. At low density, the average recognition rate increases by 6.3% with the increase of laser energy. The recognition rate first increases and then decreases with the increase of distance. When the bubbles distance is 2.2 m, the target peak characteristics are obvious, and the average recognition rate is improved by 3.5%. Compared with adaptive and additional momentum BP, this method can reduce recognition time and achieve 99.1% accuracy. It is proved that this algorithm can be effectively and widely used in the recognition of bubbles in the ships wake by lidar.